Browsing by Author "Zhang, Yunlong"

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    Data Fusion for Nonmotorized Safety Analysis
    Sener, Ipek N.; Munira, Silvy; Zhang, Yunlong (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-08)
    This project explored an emerging research territory, the fusion of nonmotorized traffic data for estimating reliable and robust exposure measures. Fusion mechanisms were developed to combine five bike demand data sources in Austin, Texas, and the fused estimate was applied in two crash analyses. The research was divided into three sequential stages. The first stage involved developing and applying a guideline to process and homogenize available data sources to estimate annual average daily bike volume at intersections. The second stage was focused on developing and applying the fusion framework—demonstrating the efficacy of multiple fusion algorithms, including two novel mechanisms, suited to the data characteristics and based on the availability of actual counts. The analysis of actual and simulated data illustrated that the fusion methods outperformed the individual estimates in most cases. In the third stage, the fused data were applied in both macro (hot-spot analysis in block group level) and micro (individual safety-related perception) models in Austin to ascertain the significance of incorporating exposure in safety analysis. While the fusion framework contributes to the research in the field of decision fusion, the demand and crash models provide insights to help stakeholders formulate policies to encourage bike activity and reduce crashes.
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    Optimal traffic control for a freeway corridor under incident conditions
    Zhang, Yunlong (Virginia Tech, 1996)
    The non-recurring congestion, caused by incidents, is the main cause of traffic delays and causes up to 60 percent of the freeway delay in the United States. When severe incidents occur on freeways, capacity reduction due to lane blockages may cause an extremely high amount of traffic delay. In many cases, parallel surface arterials are available, and provide reasonably high speed and available capacity. In this scenario, to fully utilize the corridor capacity, diversion may be practical and necessary. With the changes of traffic demand levels and patterns on surface streets due to diversion, signal retiming for surface street intersections is necessary. A nonlinear programming model was formulated to provide an integrated traffic control strategy for a freeway corridor under incident conditions. The objective function of the optimization model considers the interactions among the corridor components, and clearly reflects the primary goals of corridor traffic control under freeway incident conditions: to divert as much traffic away from the freeway as possible, not to over-congest the arterial and surface streets; and properly reset the signal timing plans at all intersections to accommodate the changed traffic demand levels and patterns. The gradient projection method is employed to solve diversion and signal retiming control measures simultaneously. By using a specifically developed simple and realistic traffic flow model and employing a sequential optimization approach, the computer program COROPT can obtain optimal traffic control strategies quickly and effectively. The COROPT program also has the flexibility to deal with various corridor configurations, different size of the corridor system, and different timing phasings. The model can address the time-varying factor of traffic flow, and can handle changing traffic and incident conditions over the time. The model performance was evaluated and validated by running the simulation and optimization programs of TRANSYT-7F and INTEGRATION. It has been found that the proposed model and control strategy reduce the overall system delay, increase the throughput of the corridor, and thus improve the traffic conditions of the entire corridor.